Tootfinder

Learning in Context: Personalizing Educational Content with Large Language Models to Enhance Student Learning
Joy Jia Yin Lim, Daniel Zhang-Li, Jifan Yu, Xin Cong, Ye He, Zhiyuan Liu, Huiqin Liu, Lei Hou, Juanzi Li, Bin Xu
https://arxiv.org/abs/2509.15068

Learning in Context: Personalizing Educational Content with Large Language Models to Enhance Student Learning
Standardized, one-size-fits-all educational content often fails to connect with students' individual backgrounds and interests, leading to disengagement and a perceived lack of relevance. To address this challenge, we introduce PAGE, a novel framework that leverages large language models (LLMs) to automatically personalize educational materials by adapting them to each student's unique context, such as their major and personal interests. To validate our approach, we deployed PAGE in a semester-…

Methodological considerations for semialgebraic hypothesis testing with incomplete U-statistics
David Barnhill, Marina Garrote-L\'opez, Elizabeth Gross, Max Hill, Bryson Kagy, John A. Rhodes, Joy Z. Zhang
https://arxiv.org/abs/2507.13531

Methodological considerations for semialgebraic hypothesis testing with incomplete U-statistics
Recently, Sturma, Drton, and Leung proposed a general-purpose stochastic method for hypothesis testing in models defined by polynomial equality and inequality constraints. Notably, the method remains theoretically valid even near irregular points, such as singularities and boundaries, where traditional testing approaches often break down. In this paper, we evaluate its practical performance on a collection of biologically motivated models from phylogenetics. While the method performs remarkably…

HairFormer: Transformer-Based Dynamic Neural Hair Simulation
Joy Xiaoji Zhang, Jingsen Zhu, Hanyu Chen, Steve Marschner
https://arxiv.org/abs/2507.12600 ht…

HairFormer: Transformer-Based Dynamic Neural Hair Simulation
Simulating hair dynamics that generalize across arbitrary hairstyles, body shapes, and motions is a critical challenge. Our novel two-stage neural solution is the first to leverage Transformer-based architectures for such a broad generalization. We propose a Transformer-powered static network that predicts static draped shapes for any hairstyle, effectively resolving hair-body penetrations and preserving hair fidelity. Subsequently, a dynamic network with a novel cross-attention mechanism fuses…